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An artificial neural network approach to bifurcating phenomena in
  computational fluid dynamics

An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

22 September 2021
F. Pichi
F. Ballarin
G. Rozza
J. Hesthaven
    AI4CE
ArXivPDFHTML

Papers citing "An artificial neural network approach to bifurcating phenomena in computational fluid dynamics"

3 / 3 papers shown
Title
Neural empirical interpolation method for nonlinear model reduction
Neural empirical interpolation method for nonlinear model reduction
Max Hirsch
F. Pichi
J. Hesthaven
32
1
0
05 Jun 2024
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
M. Khamlich
F. Pichi
G. Rozza
26
4
0
26 Aug 2023
A graph convolutional autoencoder approach to model order reduction for
  parametrized PDEs
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
F. Pichi
B. Moya
J. Hesthaven
AI4CE
30
52
0
15 May 2023
1